The meta-learning methods learn the learn rate for fine-tuning, thus we only tune the number of steps to run fine-tuning by using development data from 2 tasks (Scitail, Amazon Electronics), following [1]}, [2]}.
We found that running fine-tuning until the loss on the support set is small (\(<=0.01\) ) is an alternative that also performs competitively and does not require tuning the number of steps.
The reported results followed the previous approach and the tuned number of steps of fine-tuning for \(k=8,16,32\)  respectively were: (1) Uniform: 100,75,100 (2) Frequency: 25,150,75 (3) Cluster: 75,50,75 (4) Cluster-ccnet: 150,200,75 (5) SentCluster: 100,250,25 (6) Dynamic: 10, 100, 200. On FewRel we found 20 steps of updates on the support set to perform well on the validation data for all settings.
